IS415 Web Blog: in-class-exercise-5

Installing and Loading the R packages required:

maptools is used for creating spatial* objects
sf is for importing geospatial data
raster is for creating raster object
spatstat is for performing SPPA
tmap is for plotting thematic maps
tidyverse is for data handling

importing the Geospatial data

Importing shapefile using st_read() of sf package. The output object is in tibble sf object class.

Reading layer `MP14_SUBZONE_WEB_PL' from data source 
  `C:\JunLonggggg\junlong-is415\_posts\2021-09-13-in-class-exercise-5\data\shapefile' 
  using driver `ESRI Shapefile'
Simple feature collection with 323 features and 15 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: 2667.538 ymin: 15748.72 xmax: 56396.44 ymax: 50256.33
Projected CRS: SVY21

Projection is in SVY21

Importing aspatial data from rds folder

read_rds() of readr package is used instead of readRDS() of base R is used. This is because output of read_rds() is in tibble object.

Note that there are some data issue on childcare and chas dataframe because lat and lng should be in numeric data type. The coordinate fields seems to be in decimal degress. Hence, WGS84 referencing system is assumed, we will need to convert the reference system to EPSG3414.

Convert the aspatial data frame into sf objects

Note: The st_as_sf is able to tolerate the coordinates with character data type

Plotting a general map for reviewing

Some parameters used below: + alpha set to 0.4 to show the number of points in one geospatial location + col is to set the color of the dots + size is to set the size of the dots

Geospatial Data Wrangling

Converting from sf to Spatial* classes

as_Spatial() of sf package.

The output is in a unique dataframe format, it has combination of list and table together

The geometric property is placed under coords

Converting Spatial* data frame into Spatial* Objects

as.SpatialPoint() of as.SpatialPolygon() of maptools package

Converting from Spatial* objects into ppp objects

spatstat do not care about the geograhphical projection, this information will be dropped and loss, hence be careful how you treat the data.

Removing duplicate points using jitter

[1] FALSE

[1] FALSE

tmap do not understand ppp format, hence we need to convert back to sf.

alternative is using plot()

Extracting Punggol Planning Area

converting SpatialPolygonsDataFrame into SpatialPolygons object

Converting SpatialPolygons into owin object

Extracting spatial points window owin

L-Funtion

Generating 99 simulations of CSR  ...
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,
36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70,
71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98,  99.

Done.